The transition of human resources for health information systems from the MDGs into the SDGs and the post-pandemic era: reviewing the evidence from 2000 to 2022.

BACKGROUND: This review paper offers a policy-tracing trend analysis of national experiences among low- and middle-income countries in strengthening human resources for health information systems (HRHIS). This paper draws on evidence from the last two decades and applies a modified Bardach's policy analysis framework. A timely review of the evidence on HRHIS and underlying data systems is needed now more than ever, given the halfway mark of the Global Strategy on Human Resources for Health: Workforce 2030 and the protracted COVID-19 pandemic and other global health emergencies, over and above the increasing need for health and care workers to provide essential health services. MAIN TEXT: Considering World Health Assembly resolutions and HRH-related global developments between 2000 and 2022, we targeted peer-reviewed and gray literature covering the inception, impact, bottlenecks, and gaps of HRHIS. We also considered results from a Bill and Melinda Gates Foundation-funded project that assessed HRH data systems in 21 countries and the use of HRH data and information for policy, planning, and management. Aligned with the National Health Workforce Accounts (NHWA), we identify priority themes related to digital priorities for HRHIS and governance/leadership and present case studies of five countries that pursued different pathways to successfully develop their HRHIS. Over the last two decades, considerable progress has been achieved through a scaled-up implementation of HRHIS combined with the skills needed to analyze and use data, sustain systems functionality, and make systematic improvements over time. Global health development aid investments and technical innovations have led to advancements in HRHIS, district health information software (DHIS2), and partner collaborations during the HIV/AIDS, Ebola, and COVID-19 crises. Although the progressive implementation of NHWA continues to steer country-level efforts through standardized indicators and regular reporting, traditional challenges remain, such as data systems fragmentation, lack of interoperability between systems, and underutilization of reported data. Encouragingly, some countries demonstrate strong governance and leadership capacities and others strong HRHIS digital capacities. Both HRH and health service data are needed to inform on-demand decisions during times of emergencies and pandemics as well as during routine essential health services delivery. Evidence-based examples from distinctive countries demonstrate that reliable HRHIS is achievable for better planning and management of the health and care workforce.

An experience with the use of WISN tool to calculate staffing in a palliative care hospital in Brazil.

BACKGROUND: The article describes a healthcare staffing exercise that took place in a Cancer Hospital IV, Brazil's first public palliative care unit. There are numerous gaps in the literature on specialized cancer staffing. Palliative care is a therapy modality that should begin with the diagnosis of a chronic disease, at which point the personnel must be technically and numerically adequate, as well as well-distributed, to provide coverage of the population that requires this type of care. METHODS: The WISN tool was chosen after a systematic review of the use of workload studies in palliative care, because it fulfills this objective. The WISN method is based on a health worker's workload, was developed in the late 1990s in the health sector and has been field-tested and implemented in several countries. Direct observation was used as the fieldwork approach, which was carried out by 18 research assistants with the assistance of two supervisors. They monitored 60 professionals in seven categories for 2 weeks on weekdays in the morning and afternoon periods: nursing, pharmacy, physical therapy, medical, nutrition, psychology, and social services. RESULTS: Except for the medical staff, which at the time included additional physicians on loan from a partner institution to address a shortage in this professional group, all categories exhibited overload with WISN ratios ranging from 0.53 to 0.97. The analysis of time spent on individual activities indicated flaws with the services' informal organizations. The authors also noticed a strong emphasis on support activities and a lack of a clear schedule for training and research. The study's findings included a definition of standard activities for each professional group, an analysis and comparison of activities by categories, departments, and work shifts, a standard workload for training and research, and recommendations to include human resources planning as a fundamental part of a national policy for palliative care. CONCLUSIONS: The WISN tool can be used to plan human resources in cancer centers that provide palliative care, and it provides for a variety of analyses that can be combined with other approaches in the literature.

An assessment of existing surge capacity of tertiary healthcare system of Khyber Pakhtunkhwa Province of Pakistan using workload indicators for staffing need method.

BACKGROUND: Globally the occurrence of disasters has increased more than fourfold during the last three decades. The main concern for the healthcare system responding to a disaster is its ability to deal with the sudden influx of patients and maintaining a certain level of surge capacity. Health workers are considered to be the major driving force behind any health system. Their role gets even more prominent during disasters or public health emergencies. With the lack of information on the health workforce in the tertiary healthcare system of Khyber Pakhtunkhwa, where most of the disaster surge is diverted, it is difficult to plan and respond to accommodate the sudden surge of patients. METHODS: This was a mixed method cross-sectional survey conducted in all the tertiary care hospitals of Khyber Pakhtunkhwa province of Pakistan to assess the current staffing situation and surge capacity based on the current workload. Annual service statistics of 2018 were collected from all the tertiary care hospitals of the province. WISN was piloted with only one healthcare staff category, i.e., for doctors in Ayub Teaching Hospital before assessment in all the tertiary care hospitals was undertaken. RESULTS: Overall, there were 1215 surplus doctors in medical and allied specialties and 861 doctors in surgical and allied specialties in the tertiary healthcare system. The health care system has an acute shortage of 565 emergency department doctors. The tertiary healthcare system of KP has an overall shortage of 1099 nurses. Based on the WISN generated numbers for doctors, the tertiary care system of KP has a combined healthcare staff (doctors and nurses) that can manage an additional surge of 6.3% of patients with the current patient workload. CONCLUSION: The tertiary health care system of the Khyber Pakhtunkhwa Province of Pakistan does not possess the required ≥ 20% HR surge capacity indicating that the tertiary healthcare system is poorly prepared for disasters or public health emergencies. The lack of nursing staff, more than the doctors, is the major reason behind the lack of HR surge capacity of the tertiary health care system.

Application of workload indicators to assess the allocation of orthopedists in a national referral hospital in Brazil.

BACKGROUND: The study analyzes the allocation of specialized doctors' orthopedists in a high-complex hospital, using the WHO's Workload Indicators of Staffing Need (WISN) methodology and approach, which measures the workload pressure on the healthcare team (positive, negative, or well-adjusted). METHODS: In the first phase, the hospital's operations and activities were analyzed using the information system. The duration of the tasks performed by the specialist physicians was observed and directly measured in the second phase. Finally, the indicators were analyzed, and the workload was calculated using the WISN application. The measurement was made using the available work time per year divided by the time unit over the previous 12 months. RESULTS: The hand surgery care unit was WISN 1.0 and the ratios for the spine surgery care unit was 1.22, indicating enough physicians and no work overload among the groups surveyed. The ratio in the knee unit was 1.69, indicating that there was an excess of staffing for the workload. CONCLUSION: The workload findings and staffing calculations were useful in supporting and orienting the design and implementation of measures to increase the efficiency and effectiveness of health services.

Workload assessment of medical doctors at primary health care centers in the Duhok governorate.

BACKGROUND: A shortage in human resources, particularly physicians, has become a challenge confronting health authorities in the Duhok governorate, as these resources are the key input for delivering health care. It has become necessary to identify the most appropriate scientifically sound method for having adequate staffing levels. This study aimed to forecast the required number of physicians to cope with the current workload at the main primary health care centers in the Duhok governorate. METHODS: A cross-sectional study was adopted to collect data for 1 full year. Data collection included both primary and secondary data sources. A semi-structured questionnaire was developed to obtain information every month from health centers on activities related to training and leaves. Data analysis was performed using Workload Indicators of Staffing Need software. RESULTS: Sixty-one primary health care centers met the final criteria for analysis. The study revealed physician shortages and inequity in the distribution of staffing. In these centers, 145 physicians lacked an adequate delivery of health services based on the workload imposed on them. The 'workload indicators of staffing need' ratio was 0.33, indicating high work pressure on medical doctors. Some centers offered more health care than others, but had fewer doctors based on the current staffing practices. CONCLUSIONS: This study pointed out the importance for the public health sector and academic medical institutions to use Workload Indicators of Staffing Needs software in health policy administration to restructure their efforts to address the physician shortages and distribution imbalances at primary health care facilities.

Use of the WISN method to assess the health workforce requirements for the high-volume clinical biochemical laboratories.

BACKGROUND: The clinical laboratory services, as an essential part of health care, require appropriate staff capacity to assure satisfaction and improve outcomes for both patients and clinical staff. This study aimed to apply the Workload Indicators of Staffing Need (WISN) method for estimating required laboratory staff requirements for the high-volume clinical biochemical laboratories. METHODS: In 2019, we applied the WISN method in all 13 laboratories within the Center for Medical Biochemistry of the University Clinical Centre of Serbia (CMB UCCS). A review of annual routinely collected statistics, laboratory processes observations, and structured interviews with lab staff helped identify their health service and additional activities and duration of these activities. The study outcomes were WISN-based staff requirements, WISN ratio and difference, and a recommendation on the new staffing standards for two priority laboratory workers (medical biochemists and medical laboratory technicians). RESULTS: Medical biochemists' and laboratory technicians' annual available working time in 2019 was 1508 and 1347 working hours, respectively, for the workload of 1,848,889 samples. In general, the staff has four health service, eight support, and 15 additional individual activities. Health service activities per sample can take from 1.2 to 12.6 min. Medical biochemists and medical laboratory technicians spend almost 70% and more than 80% of their available working time, undertaking health service activities. The WISN method revealed laboratory workforce shortages in the CMB (i.e. current 40 medical biochemists and 180 medical laboratory technicians as opposed to required 48 medical biochemists and 206 medical laboratory technicians). Workforce maldistribution regarding the laboratory workload contributes to a moderate-high workload pressure of medical biochemists in five and medical laboratory technicians in nine organizational units. CONCLUSIONS: The WISN method showed mainly a laboratory workforce shortages and workload pressure in the CMB UCCS. WISN is a simple, easy-to-use method that can help decision-makers and policymakers prioritize the recruitment and equitable allocation of laboratory workers, optimize their utilization, and develop normative guidelines in the field of clinical laboratory diagnostics. WISN estimates require periodic reviews.

Applying WHO COVID-19 workforce estimate tools remotely in an African context: a case report from Mali and Kenya.

BACKGROUND: The COVID-19 pandemic has increased the burden on health systems, particularly in low- and middle-income countries, where health systems already struggle. To meet health workforce planning needs during the pandemic, IntraHealth International used two tools created by the World Health Organization (WHO) Regional Office for Europe. The Health Workforce Estimator (HWFE) allows the estimation of the quantity of health workers needed to treat patients during a surge, and the Adaptt Surge Planning Support Tool helps to predict the timing of a surge in cases and the number of health workers and beds needed for predicted caseload. These tools were adapted to fit the African context in a rapid implementation over 5 weeks in one region in Mali and one region in Kenya with the objective to test the feasibility of adapting these tools, which use a Workload Indicators of Staffing Need (WISN)-inspired human resources management methodology, to obtain daily and surge projections of COVID-19 human resources for health needs. CASE PRESENTATION: Using a remote team in the US and in-country teams in Mali and Kenya, IntraHealth enacted a phased plan to gather stakeholder support, collect data related to health systems and COVID-19 cases, populate data into the tools, verify modeled results with results on the ground, enact policy measures to meet projected needs, and conduct national training workshops for the ministries of health. CONCLUSIONS: This phased implementation in Mali and Kenya demonstrated that the WISN approach applied to the Health Workforce Estimator and Adaptt tools can be readily adapted to the local context for African countries to rapidly estimate the number of health workers and beds needed to respond to the predicted COVID-19 pandemic caseload. The results may also be used to give a proxy estimate for needed health supplies-e.g., oxygen, medications, and ventilators. Challenges included accurate and timely data collection and updating data. The success of the pilot can be attributed to the adapted WHO tools, the team composition in both countries, access to human resources data, and early support of the ministries of health, with the expectation that this methodology can be applied to other country contexts.

Applying the workload indicators of staffing needs method in nursing health workforce planning: evidences from four hospitals in Vietnam.

BACKGROUND: Vietnam has encountered difficulties in ensuring an adequate and equitable distribution of health workforce. The traditional staffing norms stated in the Circular 08/TT-BYT issued in 2007 based solely on population or institutional size and do not adequately take into consideration the variations of need such as population density, mortality and morbidity patterns. To address this problem, more rigorous approaches are needed to determine the number of personnel in health facilities. One such approach is Workload Indicators of Staffing Need (WISN) developed by the World Health Organization (WHO), a facility-based workforce planning method that assists managers in defining the responsibilities of different workforce categories and improving the appropriateness and efficiency of a staff mix. METHODS: This study applied the WISN approach and was employed in 22 clinical departments at four hospitals in Vietnam between 2015 and 2018. 22 targeted group discussions involving nurses were conducted. Hospital personnel records have been retrieved. The data were analyzed according to WISN instructions. RESULTS: Of the 22 departments, there was a shortage of 1 to 2 nurses in 10 departments, with WISN ratios ranging between 0.88 and 0.95. Only 01 clinical colleges at Can Tho Hospital lacked 05 nurses, facing a high workload with a WISN ratio of 0.78. Administrative time represented 20-40% of the total work time of a nurse. In comparison, nurses at Can Tho Hospital spent time on administration from 24 onwards. 5-41.7% of their working time while nurses at Thanh Hoa Hospital spent 21-33%. CONCLUSIONS: The application of the WISN enabled health managers to analyze the workload of nurses, calculate staffing needs, and thus effectively contribute to the workforce planning process. It is expected that the results of this research will encourage the use of the WISN tool in other hospitals and health facilities across the health system. At provincial and national levels, this study provides important evidence to help policy makers develop guidelines for personnel norms for health facilities in the context of limited resources, while the existing regulation is no longer appropriate.

Lessons learned from implementation of the Workload Indicator of Staffing Need (WISN) methodology: an international Delphi study of expert users.

BACKGROUND: Staffing of health services ought to consider the workload experienced to maximize efficiency. However, this is rarely the case, due to lack of an appropriate approach. The World Health Organization (WHO) developed and has promoted the Workload Indicators of Staffing Need (WISN) methodology globally. Due to its relative simplicity compared to previous methods, the WISN has been used extensively, particularly after its computerization in 2010. Many lessons have been learnt from the introduction and promotion of the methodology across the globe but have, hitherto, not been synthesized for technical and policy consideration. This study gathered, synthesized, and now shares the key adaptations, innovations, and lessons learned. These could facilitate lesson-learning and motivate the WHO's WISN Thematic Working Group to review and further ease its application. METHODS: The study aimed to answer four questions: (1) how easy is it for the users to implement each step of the WISN methodology? (2) What innovations have been used to overcome implementation challenges? (3) What lessons have been learned that could inform future WISN implementation? and (4) what recommendations can be made to improve the WISN methodology? We used a three-round traditional Delphi method to conduct a case study of user-experiences during the adoption of the WISN methodology. We sent three email iterations to 23 purposively selected WISN expert users across 21 countries in five continents. Thematic analysis of each round was done simultaneously with data collection. RESULTS: Participants rated seven of the eight technical steps of the WISN as either "very easy" or "easy" to implement. The step considered most difficult was obtaining the Category Allowance Factors (CAF). Key lessons learned were that: the benefits gained from applying the WISN outweigh the challenges faced in understanding the technical steps; benchmarking during WISN implementation saves time; data quality is critical for successful implementation; and starting with small-scale projects sets the ground better for more effective scale-up than attempting massive national application of the methodology the first time round. CONCLUSIONS: The study provides a good reference for easing WISN implementation for new users and for WHO to continue promoting and improving upon it.

Workforce problems at rural public health-centres in India: a WISN retrospective analysis and national-level modelling study.

BACKGROUND: Rural India has a severe shortage of human resources for health (HRH). The National Rural Health Mission (NRHM) deploys HRH in the rural public health system to tackle shortages. Sanctioning under NRHM does not account for workload resulting in inadequate and inequitable HRH allocation. The Workforce Indicators of Staffing Needs (WISN) approach can identify shortages and inform appropriate sanctioning norms. India currently lacks nationally relevant WISN estimates. We used existing data and modelling techniques to synthesize such estimates. METHODS: We conducted a retrospective analysis of existing survey data for 93 facilities from 5 states over 8 years to create WISN calculations for HRH cadres at primary and community health centres (PHCs and CHCs) in rural areas. We modelled nationally representative average WISN-based requirements for specialist doctors at CHCs, general doctors and nurses at PHCs and CHCs. For 2019, we calculated national and state-level overall and per-centre WISN differences and ratios to depict shortage and workload pressure. We checked correlations between WISN ratios for cadres at a given centre-type to assess joint workload pressure. We evaluated the gaps between WISN-based requirements and sanctioned posts to investigate suboptimal sanctioning through concordance analysis and difference comparisons. RESULTS: In 2019, at the national-level, WISN differences depicted workforce shortages for all considered HRH cadres. WISN ratios showed that nurses at PHCs and CHCs, and all specialist doctors at CHCs had very high workload pressure. States with more workload on PHC-doctors also had more workload on PHC-nurses depicting an augmenting or compounding effect on workload pressure across cadres. A similar result was seen for CHC-specialist pairs-physicians and surgeons, physicians and paediatricians, and paediatricians and obstetricians-gynaecologists. We found poor concordance between current sanctioning norms and WISN-based requirements with all cadres facing under-sanctioning. We also present across-state variations in workforce problems, workload pressure and sanctioning problems. CONCLUSION: We demonstrate the use of WISN calculations based on available data and modelling techniques for national-level estimation. Our findings suggest prioritising nurses and specialists in the rural public health system and updating the existing sanctioning norms based on workload assessments. Workload-based rural HRH deployment can ensure adequate availability and optimal distribution.

Assessing Ghana's eHealth workforce: implications for planning and training.

BACKGROUND: eHealth-the proficient application of information and communication technology to support healthcare delivery-has been touted as one of the best solutions to address quality and accessibility challenges in healthcare. Although eHealth could be of more value to health systems in low- and middle-income countries (LMICs) where resources are limited, identification of a competent workforce which can develop and maintain eHealth systems is a key barrier to adoption. Very little is known about the actual or optimal states of the eHealth workforce needs of LMICs. The objective of this study was to develop a framework to characterize and assess the eHealth workforce of hospitals in LMICs. METHODS: To characterize and assess the sufficiency of the workforce, we designed this study in twofold. First, we developed a general framework to categorize the eHealth workforce at any LMIC setting. Second, we combined qualitative data, using semi-structured interviews and the Workload Indicator of Staffing Needs (WISN) to assess the sufficiency of the eHealth workforce in selected hospitals in a LMIC setting like Ghana. RESULTS: We surveyed 76 (60%) of the eHealth staff from three hospitals in Ghana-La General Hospital, University of Ghana Hospital, and Greater Accra Regional Hospital. We identified two main eHealth cadres, technical support/information technology (IT) and health information management (HIM). While the HIM cadre presented diversity in expertise, the IT group was dominated by training in Science (42%) and Engineering (55%), and the majority (87%) had at least a bachelor's degree. Health information clerk (32%), health information officer (25%), help desk specialist (20%), and network administrator (11%) were the most dominant roles. Based on the WISN assessment, the eHealth workforce at all the surveyed sites was insufficient. La General and University of Ghana were operating at 10% of required IT staff capacity, while Ridge was short by 42%. CONCLUSIONS: We have developed a framework to characterize and assess the eHealth workforce in LMICs. Applying it to a case study in Ghana has given us a better understanding of potential eHealth staffing needs in LMICs, while providing the quantitative basis for building the requisite human capital to drive eHealth initiatives. Educators can also use our results to explore competency gaps and refine curricula for burgeoning training programs. The findings of this study can serve as a springboard for other LMICs to assess the effects of a well-trained eHealth workforce on the return on eHealth investments.

Study on the Rehabilitation Therapist Estimation Under Institutional Perspective by Applying the Workload Indicators of Staffing Needs in the Aging Context.

Background: The need for rehabilitation therapy has increased dramatically with the aging of the population, the prevalence of non-communicable diseases, and the increase in the number of disabilities. Rehabilitation therapists are crucial to provide high quality rehabilitation therapy; however, there is a significant shortage of these professionals in China. One of the effective strategies to address this challenge is using the norm of the workforce for rehabilitation therapy, which is an index for assessing the personnel required in a facility. This research aimed to create a rehabilitation therapist-required norm under institutional perspective in Shandong Province, China, based on the Workload Indicators of Staffing Needs (WISN) method, which was created by the World Health Organization (WHO) in 1998 to analyse staff utilization at various levels of the health care system. Methods: We conducted descriptive and quantitative research from October to November 2020 in the rehabilitation department of a tertiary hospital in Weifang City, China. Focus groups, online interviews, and document reviews were conducted to gather data and calculations of the WISN method performed. Results: Admission assessment, pre-treatment evaluation, rehabilitation therapy, post-treatment evaluation, and health education for patients were identified as the main priority group activities. Interviews and analysis of documents summarized five factors related to rehabilitation therapists' health service activities. In this study, the annual working time of each therapist was 1,776 h per year. The WISN method calculations showed that the norm of rehabilitation therapists in this tertiary hospital was 23 therapists. As the department had 13 therapists, there was a shortage of 10 therapists based on the WISN calculation, with a ratio of 0.57, which represented the actual compared to the ideal number of therapists. Conclusion: Workload pressure was high for therapists in this tertiary hospital. This model revealed a demand for ten more therapists in the rehabilitation department. The WISN method can help hospital administrators in therapist workforce monitoring, including in regard to therapists. Therefore, the WISN method should be embraced as part of hospital human resource planning and recruitment strategies to meet increasing rehabilitation needs.

Lessons Learnt during the Implementation of WISN for Comprehensive Primary Health Care in India, South Africa and Peru.

INTRODUCTION: The World Health Organization introduced the workload indicators of staffing needs (WISN) in 1998 to improve country-level health workforce planning. This study presents the primary care health workforce planning experiences of India, South Africa and Peru. METHODS: A case study approach was used to explore the lessons learnt in the implementation of WISN in India and South Africa. It also describes the methods developed and implemented to estimate health workforce in Peru. We identify the barriers and facilitators faced by countries during the implementation phase through the triangulation of literature, government reports and accounts of involved health planners in the three countries. RESULTS: India implemented WISN in a referral pathway of three district health facilities, including a primary health centre, community health centre and district hospital. Implementation was impeded by limited technical support, poor stakeholder consultation and information systems challenges. South Africa implemented WISN for health workforce planning in primary care and found the skills mix and staff determinations to be unaffordable. The Peruvian Ministry of Health considered using WISN but decided to develop a context-specific tool to estimate the health workforce needed using its available resources such as the National Register of Health Personnel. The main challenge in using WISN was the insufficient information on its inputs. CONCLUSION: While India and South Africa had unique experiences with the integration of WISN in their health system, none of the countries has yet benefited from the implementation of WISN due to financial, infrastructure and technical challenges. Since the methodology developed by the Peruvian Ministry of Health is context-specific, its implementation has been promising for health workforce planning. The learnings from these countries' experiences will prove useful in bringing future changes for the health workforce.

Health Service Activity Standards and Standard Workloads for Primary Healthcare in Ghana: A Cross-Sectional Survey of Health Professionals.

The attainment of health system goals is largely hinged on the health workforce availability and performance; hence, health workforce planning is central to the health policy agenda. This study sought to estimate health service activity standards and standard workloads at the primary health care level in Ghana and explore any differences across health facility types. A nationally representative cross-sectional survey was conducted among 503 health professionals across eight health professions who provided estimates of health service activity standards in Ghana's Primary Health Care (PHC) settings. Outpatient consultation time was 16 min, translating into an annual standard workload of 6030 consultations per year for General Practitioners. Routine nursing care activities take an average of 40 min (95% CI: 38-42 min) for low acuity patients; and 135 min (95% CI: 127-144 min) for high dependency patients per inpatient day. Availability of tools/equipment correlated with reduced time on clinical procedure. Physician Assistants in health centres spend more time with patients than in district hospitals. Midwives spend 78 min more during vaginal delivery in health centres/polyclinics than in district/primary hospital settings. We identified 18.9% (12 out of 67) of health service activities performed across eight health professional groups to differ between health centres/polyclinics and district/primary hospitals settings. The workload in the health facilities was rated 78.2%, but as the workload increased, and without a commensurate increase in staffing, health professionals reduced the time spent on individual patient care, which could have consequences for the quality of care and patient safety. Availability of tools and equipment at PHC was rated 56.6%, which suggests the need to retool these health facilities. The estimated standard workloads lay a foundation for evidence-based planning for the optimal number of health professionals needed in Ghana's PHC system and the consequent adjustments necessary in both health professions education and the budgetary allocation for their employment. Finally, given similarity in results with Workload Indicators of Staffing Need (WISN) methodology used in Ghana, this study demonstrates that cross-sectional surveys can estimate health service activity standards that is suitable for health workforce planning just as the consensus-based estimates advocated in WISN.